Comparison of Various Variant Prediction Algorithms and Conservation Scores by Machine Learning Approaches

Sombuddha Roy Bhowmick

doi:10.36948/ijfmr.2024.v06i05.27138

Comparison of Various Variant Prediction Algorithms and Conservation Scores by Machine Learning Approaches

Author(s)	Sombuddha Roy Bhowmick
Country	India
Abstract	Machine learning methods are widely used for prediction of the pathogenicity and deleteriousness of the different types of single nucleotide variants (SNVs). This study encompasses a preliminary approach towards the application of different types of machine learning algorithms on the dbNSFPv4.7 (database of Nonsynonymous SNPs Functional Predictions) database to compare the various prediction algorithms and conservation scores to determine which one performs the best. This paves the way for selecting the best prediction algorithm or the combination of the better prediction algorithms in annotation of the variants and classifying them as pathogenic or likely pathogenic, benign or likely benign. The different learning approaches were compared based on the metrices like, accuracy, recall, precision and F1 score. Among the different variant prediction algorithms and conservation scores available in the dbNSFP database, BayesDel AF was the best perfoming followed by BayesDel noAF, MetaRNN, MCAP and MutPred. bStatistic, fitCons, fathmm XF, MutationTaster and phastCons17way primate performed the worst among 31 prediction algorithms and 9 conservation scores selected for this study. The best performing features or instances can be prioritized over others while selecting the functional prediction algorithms, which are used to determine the driver mutations or the pathogenic variants from omics datasets.
Keywords	Machine Learning, Prediction Algorithms, Conservation Scores, Single Nucleotide Variants (SNVs), dbNSFP
Field	Biology
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-09-05
Cite This	Comparison of Various Variant Prediction Algorithms and Conservation Scores by Machine Learning Approaches - Sombuddha Roy Bhowmick - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.27138
DOI	https://doi.org/10.36948/ijfmr.2024.v06i05.27138
Short DOI	https://doi.org/gwfgrm

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6 Cover Page Vol 6 Isu 5 Cover Page Vol 6 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Comparison of Various Variant Prediction Algorithms and Conservation Scores by Machine Learning Approaches

Share this